COVID-19 pandemic and unemployment rate prediction for developing countries of Asia: A hybrid approach

Unemployment is an essential problem for developing countries, which has a direct and major role in economy of a country. Understanding the pattrens of unemployment rate is critical now a days and has drawn attention of researcher from all fields of study across the globe. As unemployment plays an important role in the planning of a country’s monetary progress for policymakers and researcher. Determining the unemployment rate efficiently required an advance modeling approach. Recently,numerous studies have relied on traditional testing methods to estimate the unemployment rate. Unemployment is usually nonstationary in nature. As a result, demonstrating them using traditional methods will lead to unpredictable results. It needs a hybrid approach to deal with the prediction of unemployment rate in order to deal with the issue associated with traditional techniques. This research primary goal is to examine the effect of the Covid-19 pandemic on the unemployment rate in selected countries of Asia through advanced hybrid modeling approach, using unemployment data of seven developing countries of Asian: Iran, Sri Lanka; Bangladesh; Pakistan; Indonesia; China; and India,and compare the results with conventional modeling approaches. Finding shows that the hybrid ARIMA-ARNN model outperformed over its competitors for Asia developing economies. In addition, the best fitted model was utilised to predict five years ahead unemployment rate. According to the findings, unemployment will rise significantly in developing economies in the next years, and this will have a particularly severe impact on the region’s economies that aren’t yet developed.


Introduction
To evaluate financial models and select reasonable venture tactics, monetary benefactors utilise company data and financial estimates, such as total national output. To put it another way, the unemployment rate converts into an appropriate monetary indicator for every country because of its connection to the state yield plan and the influence it has on monetary policy [1]. An organisation takes very specific efforts to ensure that its monetary concerns are properly managed and formalises a framework for doing so. Beginning in the mid-1990s, research out-of-test gauges for Canadian unemployment rates [22], demonstrating the ARIMA model's value and feasibility. The situation was in any event imperceptibly erratic when compared to the US unemployment rate. TAR, an old-style, non-straight time plan model, avoided models of direct time game-plans to keep the US unemployment rate from running out of data [23]. Direct models are evaded by non-straight models [24,25] for the transitory assessment of irregularly changing US unemployment statistics records from month to month. Improvements in the field of current assessments and PC-based insight have equipped the markers with non-straight estimate instruments, including, for example, Artifical Neural Association (ANN), Supervised Learning and Support Vector Machines (SVM) [26,27]. For the United States, Canada, the Bound together Domain, France, and Japan, ANN is mostly used to measure unemployment rather than a contradictory money-related time [28,29]. According to previous findings, non-random models are well-suited to longer evaluation horizons because of their ability to capture unemployment rate time course of action unevenness [30]. The unemployment rate has historically been reported in an asymmetrical manner, and this will undoubtedly continue [31].
When it comes to forecasting non-linear time series, the typical ARIMA model is terrible, but Artifical Neural Networks have recently shown excellent results. Rise of artificial intelligence increases present-day cataloging capacity and opens the door to new, vexing use cases one of the most well-known exploration areas in the advanced era of enormous information is the usage of astute based hybrid methods for large big data [32]. Soft reasoning was used to add up the reactions of a few expectation models, working on the final projection by wonderfully connecting the module yields. In the time spent deciding on a final forecast, soft reasoning takes care of the vulnerability. To match the Coronavirus data for Heilongjiang province in China, researchers tweaked a traditional differential condition model. For developing nations, [8,33] presented a clever hybrid prediction technique that used a mixture of hypothetical concepts and soft reasoning. The inventor was able to assess the complexity of the nonrandom behaviour of Covid time series for various countries using the numerical meaning of the hybrid approach. [32] developed a new calculating method based on artifical neural networks. Furthermore, [34] suggested using an autoregressive neural organisation method. Extricate strategy checks serve as data sources for an ARNN [35]. Less randomness and simpler interpretation are two advantages over the ANN plan [36] that this strategy provides. Close data records provide both direct and non-straight examples in the recent worry about evaluating the unemployment rate. It's critical that policymakers stay focused on a single model in order to witness a discontinuous shift in the overall behaviour of unemployment rates. Segment models' propensity and change in assumption goof can be reduced by combining instantaneous and non-straight models [37]. As per this, a blend of immediate and nondirect models is utilized for the particular model of such confounded auto-affiliation structures [38,39]. Previous models were used to deal with various assessment difficulties in the insurance exchange, money-related econometrics, control, contamination-causing assessment and different application zones [40][41][42][43]. The combination of ARIMA-ARNN is the strongest guide for unemployment during the disproportionate market cycle for European countries [44,45]. These above-mentioned mixing models are crucial in attempting to resolve verifiable measurement difficulties over the long term.
This study's main goal is to investigate the connection between the direct and non-straight component of the unemployment rate's time series using innovative crossover methods. Direct and non-straight sections of the provided series are expected to be separately shown by distinct models as part of the mix approach, which retains a particularly strong link between them. By then, the results will be combined. When looking at the nonlinear nature of this time game plan, hybrid approaches are more suited for examining the varying unemployment rate patterns. The time series is extended out to an ARIMA model in half-breed models. The leftovers from the fitted ARIMA model are also shown using contemporary AI techniques to achieve excellent expectation accuracy. This is followed by the use of non-direct models such as ARIMA's evaluated arbitrariness errors and ANN, SVM, and ARNN to display non-straight instances in the information gathering. This investigation develops a strong method to crosscountry demonstrating and is used to estimate future unemployment rates.
The remainder of this study is organised in the following manner. Section 2 presents a description of the unemployment rate data set for several developing nations, as well as planned techniques for analysing the data. Section 3 discusses the hybrid modelling techniques that have been developed. When it comes to policy implementation, Section 4 demonstrates the application of the suggested hybrid method to real data sets, and Section 5 closes this research with policy recommendations.

Research methodology
As a level of the economy, the unemployement rate refers to the measure of unemployement. The valuation of unemployement rate can be described as the comprehensive prize as a workforce level for the measure of jobless individuals. In this study, seven sessionally changed month to month time series information on unemployement rates were used for Pakistan, Iran, China, Bangladash, India, Sirlanka and Indonesia. The depiction of unemployement rate informational indexes are introduced in Table 1 underneath for every chose country. Fig 1a-1g shows the time plots for several emerging countries. There is non-stationarity and non-linearity in the data, which is supported by the unemployment rate's visualisation. It covers the broadest range of nonlinearity, thus we applied a linearity test to confirm its validity [45]. For all seven sets of findings, the linearity test strongly contradicts our linearity hypothesis.
In this investigation, we estimate unemployment rate using only one variable: the number of unemployed people. Some of the usual deviations from conventionality and nonlinearity in the data organisation may be seen in these time series, as evidenced by previous studies [25,26,31,46,47]. In the beginning, the non-linearity was obtained using the direct ARIMA model. Hybrid models were used to better model the nonlinearity exposed in the early stages, resulting in excellent prediction accuracy.

Reseach methodology
This study utilized novel hybrid models approach based on ARIMA, ANN, SVM and ARNN to predict five year ahead unemployment rate for developing nations of Asia.  model as seen follows Whereas, y t proves the actual valuation of the inconstant possible at fact t, ε t is random error at time t, ; i and α j are the coefficients of the model. The ARIMA model builds a random temporal structure knowledge set in the following phases: Auto-correlation Function (ACF) plots determined the lag order of the underline model [36,[48][49][50][51].
2.1.2. ARNN model. An ANN each of the three layers (info, hidden, and output) represents a different neural network design. From one layer to another, data is sent using a riskreduction "learning calculation." A predetermined number of veiled neurons are used in the construction of the ARNN model for time-arrangement knowledge collection [35]. The model now includes previously rejected temporal arrangement computations. It has one concealed layer p and k hidden units in the hidden layer. BIC is also used to analyse ARNN (p, k) models. At this timeẑ t is calculated with preferred precedent inspections z t−j1 ,. . ., z t−jp Like the contributions. From one camouflaged sheet, the ARNN model will become clear.
where {w c k } indicates the concerning weights and ; i is the creation task. Weights of the ARNN model are taught by means of a incline fall reverse broadcast algorithm [35]. The ARNN (p, k) model utilizes p as the figure of lags for an AR(p) model, and k is frequently set to k ¼ pþ2 for non-cyclic time series data [36]. [52] (SVMs). Consistent with SRM, SVMs aim to reduce the scope of hypothetical blunder in exchange for more grounded blunder. Moreover, the SVM models provide the retreat utility by increasing the computation direct capacity over time. The SVM has the following relapse capacity:

SVM-model. As per Vapnik
Such as ϕ(x) is distinguished the trademark, which is non-direct arranged from the investment hole x. The coefficients w and b are unsurprising by lessening where both C and ε are prescribed parameters. The first term L ε (d, z) is called the ε-intensive loss function. The d i is the actual stock price in the i th period. This function indicates that errors below ε are not penalized. The term C 1 In the second term, 1 2 jjwjj 2 , measures the evenness of the capacity. C evaluates the compromise between the observational danger and the levelness of the model. Presenting the positive leeway factors γ and γ � , which represent the distance from the actual values to the corresponding boundary values of ε-tube. Eq 3 is transformed into the following constrained formation: Minimize Subjected to At long last, presenting Lagrangian multipliers and expanding the double capacity of Eq 5 changes Eq 6 to the accompanying structure: with the constraints In Eq 9, β i and b � i are called Lagrangian multipliers. They satisfy the equalities Here, K(y, y i ) is called the kernel function. The value of the kernel is equal to the inner product of two vectors x i and x j in the feature space ϕ(x i ) and ϕ(x j ), such that K(y, y) = ϕ(y i ) � ϕ (y j ). Any function that satisfying Mercer's condition [49,52,53] can be used as the Kernel function. The Gaussian kernel function is determined in this study. SVMs were used to examine the anticipated informational index's non-direct behaviour since Gaussian pieces perform well under broad perfection suspicions.

ANN-model.
ANNs are adaptable procedures for modeling non-linear pattren. ANN models have the advantage over other non-direct models of being uniform approximations dictated by several limitations. Their strength comes from the repetition of the same information. They may eliminate any prior model suspicions during model development. The information characteristics can also affect the organisation model. The most often used model for predicting and forecasting chronological data is artificial neural networks [54]. The model is composed of three lyers of basic to-give units linked by a repeating connection. The interaction among the produce (z t ) and the inputs (z t−1 , z t−2 ,. . ., z t−p ) has the following mathematical illustrations: where j (j = 0,1,2,. . .,q) and β ij (i = 0,1,2,. . .,p; j = 0,1,2. . .,q),here p is the number of information hubs and q the number of hidden hubs. In other words, the strategic capability is often used as a covert layer manoeuvre.
Hence, the ANN model of Eq 13 in fact, performs a non-linear functional mapping from the past observations (z t−1 , z t−2 ,. . ., z t−p ) to the future value z t , i.e., A vector containing all parameters, and a function of the network design and connection weights. So a non-linear autoregressive model is a network. For one-step-ahead estimate, Eq 13 means one output node.

Hybrid modeling approach
Hybrid demonstrating is used in two phases to handle nonstationary time series. Initially, the immediate component of the time series was shown using the ARIMA(p,d,q) model, and then the non-linear component was modelled using advanced approaches ANN, SVM, and ARNN models. Two findings are combined to get a point estimate. The hybrid modelling approach uses machine learning models to assess patterns. An analysis of unemployment rates in developing nations of Asia using hybrid ARIMA-ANN, ARNN, and SVM models.

Hybrid ARIMA-ARNN model
We utilised a two-stage ARIMA-ARNN model. Initially, an ARIMA model is employed to forecast the linear component of the data. In addition, the ARNN technique was used to model nonlinear part of the data. The preparation of the planned hybrid ARIMA-ARNN model (M t ) can be expressed as: where Y t is the linear fraction, and N t is the non-linear element of the combination model. We can estimate Y t and Zt together from the training data set. Let,Ŷ t be the anticipate cost of the ARIMA model at time t plus a t stands for the error outstandings at point t, found from the ARIMA model. We can after that mark The ARNN model represented the remaining section as follows: for an observation of size n, Where, h is a non-linear ARNN model function and γ t is the random errors. So we can write the mutual estimation as:Q Where ARNN modelM t is standard. ARNN models the missing auto-relationships in ARIMA. The direct ARIMA model's ability to generate repeated measure is limited by model disregard measures and unemployment rate time series. If the mixture series is recreated, the unpredictable indicators' efficiency can be slightly increased. The same two-phase method is used for ARIMA-ANN and ARIMA-SVM. The algorithmic presentation of mixing expectation is as follows:

Algorithm of Hybrid modeling approach for unemployment rate prediction
Step-1 Initiate: Divide the data into two groups: {Training} + {Testing} Step-2 Accomplished: Implement and select best ARIMA (p, d, q) model by means of training data -Parameters of ARIMA model (p, d and q) were selected by ways information criteria (BIC, AIC etc.) -Acquired prediction of ARIMA model using training observation.
-Using ARIMA prediction achieve random error (ât) Step-3 Accomplished: Acquire best ANN model from training observation residuals -Accomplished lag selection on training data set residuals and then apply ANN model with p selected lagged input from residuals and k hidden units.
-Acquire prediction of residuals using ANN model Step-4 Estimation of unemployment rate(Q t ): Combine both the predictions(ARIMA and ANN models) to obtain final prediction.

Data and computational environment
The data utilised in this study came from the FRED Financial Informational Indexes database, accessible at https://fred.stlouisfed.org. The work shown here was done in R-studio, a free online statistical programming tool. The "forecast" programme calculated a traditional ARIMA (p, d, q). It uses the "e1071" package for SVM and the "nnetar" function for ARNN. The "mlp" function calculated the ANN model. In artificial neural networks, we assume one hidden layer.

Performance evaluation metrics of hybrid-models
The mean-absolute error (MAE), mean-absolute percent error (MAPE), and root-mean square error (RMSE) of [41] are used to assess the unemployment rate precditions models. These measurements are expressed quantitatively as; RMSE ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi 1 n where B i is the actual output,B i is the observed output, and "n" is the number of time varing observations. Table 1 shows two groupings of observations for the unemployment rate in seven Asian nations. The underlined time series are non-stationary and non-linear, as measured (see segment. 2.1). Fig 1 shows the nonlinearity of these datasets for developing Asian nations. To compare the models, we used the ARIMA-ARNN model from [55], the ARIMA-ANN model from [36], and the hybrid ARIMA-SVM model from [42].

Results and discussion
We employed the "predict" package to best fit the ARIMA model in the R-studio computational environment. For this reason, we must first define p, d, and q (the model). Fig 2 shows the graph of the order selection of ARIMA for each time series using ACF and PACF to better fit the data. In the ARIMA model, d was the lag at which the data became stationary. The best ARIMA model for each developing country is chosen using AIC and log-likelihood ( Table 2). The best ARIMA model was then used for one and five year ahead forecast. We also obtained random errors from projected training outcomes. In the next stage, we modelled ARIMA residuals with ARNN (p, k) model. To make the estimates positive, we set the Case Cox modification to 0 earlier. The assessment of p and k is characterised by following the suggestion of [36,[56][57][58]. To achieve the final findings, we merged the direct ARIMA model with non-linear ARNN output. Also, in this research, Support Vector Machines (SVM) and their Artificial Neural Network (ANN) combination were provided. We used one hidden layer for the ANN model's to make the calculation simple of 'kn' neurons, where "n" is the sample size of training data set. The ARIMA (1,1,2) model has the greatest AIC and log-likelihood values of -155.28 and 85.67. The ARNN (3,2) model was then calibrated to ARIMA residuals, with an average of 16 networks (1,1,2). Using the testing observations, we created a hybrid ARI-MA-ARNN, ARIMA-ANN, and ARIMA-SVM model to predict the future. Table 3 shows the performance metrics for one and five years ahead for hybrid vehicles in China. Similarly, the ARIMA (2,1) model fits the Pakistani training data set better, with AIC = -464.05 and L = 333.03. An ARNN (3,1) model was tuned to ARIMA results using a typical 16 network with two weights each (2,1,1). The final prediction uses the ARIMA and ARNN anticipated effects to estimate the MAE, MAPE, and RMSE. Table 3 shows Pakistan's performance matrices. Sirlanka's ARIMA (2,1,2) model has an AIC of -688.11 and a log probability (L) of 551. It was also fitted to the ARIMA residuals in stage one, which is a 19-9-1 network by 150 weights, using an average of 16 networks. Finally, the final prediction combines the ARIMA and ARNN projections. Table 3 presents RMSE, MAE, and MAPE as a function of final anticipated value. Table 3 shows the results of applying all forecasting models to the India and Bangladesh data sets. We simulate the ARIMA residuals with an ARNN (16,4) model with a 16-network intermediate. We used ARIMA (1,1,1) residuals to fit an ARNN (12,5) model on Indonesian monthly data (1,1,1). Table 3 shows relevant results. AIMA(1,1,2) also fit Iran. The ANN model was used to adjust the residuals from each ARIMA model. The final forecast is made by combining the findings from each instance using the ARIMA model. The model's performance is evaluated using MAE, RMSE, and MAPE values, as shown in Table 3.
In Table 3, relevant modelling methods including ARIMA, ANN, ARNN, and SVM were used on unemployment data sets from Asia's developing nations . Fig 3a-3g attract and provide forecasts for the testing observation of the best appropriate hybrid model for chosen nations data.
Its figure, precision is higher than any remaining people and hybrid models for all developing countries in Asia. For most countries,hybrid ARIMA-SVM and hybrid ARIMA-ANN perform comparatively, though ARIMA-ANN out performe among all models utilized for Asian developing countries. The exposure in China's unemployment rate is somewhat low contrasting with other Asian countries since China's unemployment rate time series is mostly utilizing hybrid ARIMA-ANN model. The most dire outcome imaginable is India, where the Covid produces gigantic joblessness and has expanded the current jobless rate. The best fitting models were utilized to produce one-year and five-year projections for every selected seven countries, as displayed in Table 4. The point forecast shows that the unemployment rate in chosen Asian countries would remain high for the following three years, prior to declining and balancing out following five years.

Conclusion and implementations
The unemployment rate is a reliable indicator of the job market environment. The monthly unemployment rate of a region is one of the most important financial events for investors. The influence of the unemployment rate on stock returns varies depending on the country's economy. Concluding the unemployment rate accurately helps consumers avoid market risk from surprising changes in project conditions and economic care. This study builds on our prior research on the effects of corona virus on global unemployment [46,49,50,[59][60][61]. In this study, we used ARIMA, ANN, SVM, and ARNN hybrid models to predict feasible unemployement. With the hybrid modeling approach, the systematic and non-systematic parts of the unemployement rate were successfully demostrated. Because the unemployment rate is constant in some countries, ARNN adjusts the residuals from the fitted ARIMA model. Because of the technique's limited disclosures, these iterative methods provide extremely dependable and precise results which is in line with the findings of Castillo [33,58]. A closer look at the data reveals several interesting findings: first, almost all of the countries included in this study have had no consistent pattern of unemployment over the years; second, the hybrid-based modelling approach improved both long-term and short-term objectives, and improved individual univariate forecasting.

Future research work
The scientific community must immediately collaborate to develop new and improved methodologies, tactics, forecasting tools, and models to better understand and control pandemic consequences. Based on the findings of this study, the responsible authority should adopt particular policies, especially in developing countries, to address the problem of corona virus unemployment. Concrete strategies are necessary for commercial and economic survival following the Corona virus in both developed and developing nations.